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Patnaik A, Guruprasad N, Sekar A, Bansal S, Sahu RN. An Observational Comparative Study to Evaluate the Use of Image-Guided Surgery in the Management and Outcome of Supratentorial Intracranial Space-Occupying Lesions. JOURNAL OF PHARMACY AND BIOALLIED SCIENCES 2024; 16:S589-S591. [PMID: 38595518 PMCID: PMC11001000 DOI: 10.4103/jpbs.jpbs_881_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 09/16/2023] [Accepted: 09/20/2023] [Indexed: 04/11/2024] Open
Abstract
Objectives The objective of this article is to study the effect of neuronavigation on the outcome of surgery for supratentorial tumors, such as the extent of resection, size of craniotomy, and overall morbidity and mortality by comparing with conventional excision. Methods A total of 50 patients undergoing intracranial surgery for supratentorial space-occupying lesions from 2020 to 2022 were included in the study. One intervention group consisted of patients undergoing surgical resection of supratentorial tumors utilizing image guidance versus the control group, which consisted of patients undergoing surgical excision of supratentorial tumor excision without image guidance. Parameters used to compare the outcome were the extent of resection of the lesions, craniotomy size, and overall morbidity and mortality. Results and Conclusion There was no significant reduction in craniotomy size or prolongation of operative duration with the use of neuronavigation. There was no significant difference in postoperative hospital stay between the two groups. Neuronavigation-assisted cases did not show any significant reduction in the occurrence of postoperative neurological deficits or any reduction of overall morbidity and mortality.
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Affiliation(s)
- Ashis Patnaik
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - N Guruprasad
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Arunkumar Sekar
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Sumit Bansal
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Rabi N. Sahu
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
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Zhou C, Cha T, Peng Y, Li G. Transfer learning from an artificial radiograph-landmark dataset for registration of the anatomic skull model to dual fluoroscopic X-ray images. Comput Biol Med 2021; 138:104923. [PMID: 34638020 DOI: 10.1016/j.compbiomed.2021.104923] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/19/2021] [Accepted: 10/04/2021] [Indexed: 01/01/2023]
Abstract
Registration of 3D anatomic structures to their 2D dual fluoroscopic X-ray images is a widely used motion tracking technique. However, deep learning implementation is often impeded by a paucity of medical images and ground truths. In this study, we proposed a transfer learning strategy for 3D-to-2D registration using deep neural networks trained from an artificial dataset. Digitally reconstructed radiographs (DRRs) and radiographic skull landmarks were automatically created from craniocervical CT data of a female subject. They were used to train a residual network (ResNet) for landmark detection and a cycle generative adversarial network (GAN) to eliminate the style difference between DRRs and actual X-rays. Landmarks on the X-rays experiencing GAN style translation were detected by the ResNet, and were used in triangulation optimization for 3D-to-2D registration of the skull in actual dual-fluoroscope images (with a non-orthogonal setup, point X-ray sources, image distortions, and partially captured skull regions). The registration accuracy was evaluated in multiple scenarios of craniocervical motions. In walking, learning-based registration for the skull had angular/position errors of 3.9 ± 2.1°/4.6 ± 2.2 mm. However, the accuracy was lower during functional neck activity, due to overly small skull regions imaged on the dual fluoroscopic images at end-range positions. The methodology to strategically augment artificial training data can tackle the complicated skull registration scenario, and has potentials to extend to widespread registration scenarios.
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Affiliation(s)
- Chaochao Zhou
- Orthopaedic Bioengineering Research Center, Department of Orthopaedic Surgery, Newton-Wellesley Hospital, Newton, MA, USA; Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas Cha
- Orthopaedic Bioengineering Research Center, Department of Orthopaedic Surgery, Newton-Wellesley Hospital, Newton, MA, USA; Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yun Peng
- NuVasive Inc, San Diego, CA, USA
| | - Guoan Li
- Orthopaedic Bioengineering Research Center, Department of Orthopaedic Surgery, Newton-Wellesley Hospital, Newton, MA, USA.
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Process analysis and application summary of surgical navigation system. JOURNAL OF COMPLEXITY IN HEALTH SCIENCES 2020. [DOI: 10.21595/chs.2020.21265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Ghafurian S, Hacihaliloglu I, Metaxas DN, Tan V, Li K. A computationally efficient 3D/2D registration method based on image gradient direction probability density function. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.07.070] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Galloway RL, Herrell SD, Miga MI. Image-Guided Abdominal Surgery and Therapy Delivery. JOURNAL OF HEALTHCARE ENGINEERING 2012; 3:203-228. [PMID: 25077012 PMCID: PMC4112601 DOI: 10.1260/2040-2295.3.2.203] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Image-Guided Surgery has become the standard of care in intracranial neurosurgery providing more exact resections while minimizing damage to healthy tissue. Moving that process to abdominal organs presents additional challenges in the form of image segmentation, image to physical space registration, organ motion and deformation. In this paper, we present methodologies and results for addressing these challenges in two specific organs: the liver and the kidney.
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Affiliation(s)
- Robert L. Galloway
- Department of Biomedical Engineering
- Department of Neurosurgery
- Department of Surgery
| | | | - Michael I. Miga
- Department of Biomedical Engineering
- Department of Neurosurgery
- Department of Radiology and Radiological Sciences Vanderbilt University
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An Image-Guided Surgery System to Aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience. ACTA ACUST UNITED AC 2011. [DOI: 10.1007/978-3-642-21504-9_2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Ruijters D, ter Haar Romeny BM, Suetens P. Vesselness-based 2D-3D registration of the coronary arteries. Int J Comput Assist Radiol Surg 2009; 4:391-7. [PMID: 20033586 DOI: 10.1007/s11548-009-0316-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2009] [Accepted: 04/15/2009] [Indexed: 11/28/2022]
Abstract
PURPOSE Robust and accurate automated co-registration of the coronary arteries in 3D CTA and 2D X-ray angiography during percutaneous coronary interventions (PCI), in order to present a fused visualization. METHODS A novel vesselness-based similarity measure was developed, that avoids an explicit segmentation of the X-ray image. A stochastic optimizer searches the optimal registration using the similarity measure. RESULTS Both simulated data and clinical data were used to investigate the accuracy and capture range of the proposed method. The experiments show that the proposed method outperforms the iterative closest point method in terms of accuracy (average residual error of 0.42 mm vs. 1.44 mm) and capture range (average 71.1 mm/20.3 degrees vs. 14.1 mm/5.2 degrees ). CONCLUSION The proposed method has proven to be accurate and the capture range is ample for usage in PCI. Especially the absence of an explicit segmentation of the interventionally acquired X-ray images considerably aids the robustness of the method.
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Affiliation(s)
- Daniel Ruijters
- Philips Healthcare, Cardio/Vascular Innovation, Best, The Netherlands.
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Russakoff DB, Rohlfing T, Mori K, Rueckert D, Ho A, Adler JR, Maurer CR. Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2005; 24:1441-54. [PMID: 16279081 DOI: 10.1109/tmi.2005.856749] [Citation(s) in RCA: 64] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-limiting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF necessary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate registration accuracy using gold-standard clinical spine image data from four patients. The registration accuracy and robustness of the two methods is virtually identical whereas the execution speed using AF-DRRs is an order of magnitude faster.
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Affiliation(s)
- Daniel B Russakoff
- Department of Computer Science, Stanford University, Stanford, CA 94305 USA.
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Pauchard Y, Smith MR, Mintchev MP. Improving geometric accuracy in the presence of susceptibility difference artifacts produced by metallic implants in magnetic resonance imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2005; 24:1387-99. [PMID: 16229424 DOI: 10.1109/tmi.2005.857230] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Geometric and intensity distortions due to the presence of metallic implants in magnetic resonance imaging impede the full exploitation of this advanced imaging modality. The aim of this study is to provide a method for (a) quantifying and (b) reducing the implant distortions in patient images. Initially, a set of reference images (without distortion) was obtained by imaging a custom-designed three-dimensional grid phantom. Corresponding test images (containing the distortion) were acquired with the same imaging parameters, after positioning a specific metallic implant in the grid phantom. After determining: 1) the nonrecoverable; 2) the distorted, but recoverable; and 3) the unaffected areas, a point-based thin-plate spline image registration algorithm was employed to align the reference and test images. The calculated transformation functions utilized to align the image pairs described the implant distortions and could therefore be used to correct any other images containing the same distortions. The results demonstrate successful correction of grid phantom images with a metallic implant. Furthermore, the calculated correction was applied to porcine thigh images bearing the same metallic implant, simulating a patient environment. Qualitative and quantitative assessments of the proposed correction method are included.
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Affiliation(s)
- Yves Pauchard
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada
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Russakoff DB, Rohlfing T, Adler JR, Maurer CR. Intensity-based 2D-3D spine image registration incorporating a single fiducial marker. Acad Radiol 2005; 12:37-50. [PMID: 15691724 DOI: 10.1016/j.acra.2004.09.013] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2004] [Revised: 09/13/2004] [Accepted: 09/25/2004] [Indexed: 11/18/2022]
Abstract
RATIONALE AND OBJECTIVES The two-dimensional (2D)-three dimensional (3D) registration of a computed tomography image to one or more x-ray projection images has a number of image-guided therapy applications. In general, fiducial marker-based methods are fast, accurate, and robust, but marker implantation is not always possible, often is considered too invasive to be clinically acceptable, and entails risk. There also is the unresolved issue of whether it is acceptable to leave markers permanently implanted. Intensity-based registration methods do not require the use of markers and can be automated because such geometric features as points and surfaces do not need to be segmented from the images. However, for spine images, intensity-based methods are susceptible to local optima in the cost function and thus need initial transformations that are close to the correct transformation. MATERIALS AND METHODS In this report, we propose a hybrid similarity measure for 2D-3D registration that is a weighted combination of an intensity-based similarity measure (mutual information) and a point-based measure using one fiducial marker. We evaluate its registration accuracy and robustness by using gold-standard clinical spine image data from four patients. RESULTS Mean registration errors for successful registrations for the four patients were 1.3 and 1.1 mm for the intensity-based and hybrid similarity measures, respectively. Whereas the percentage of successful intensity-based registrations (registration error < 2.5 mm) decreased rapidly as the initial transformation got further from the correct transformation, the incorporation of a single marker produced successful registrations more than 99% of the time independent of the initial transformation. CONCLUSION The use of one fiducial marker reduces 2D-3D spine image registration error slightly and improves robustness substantially. The findings are potentially relevant for image-guided therapy. If one marker is sufficient to obtain clinically acceptable registration accuracy and robustness, as the preliminary results using the proposed hybrid similarity measure suggest, the marker can be placed on a spinous process, which could be accomplished without penetrating muscle or using fluoroscopic guidance, and such a marker could be removed relatively easily.
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Affiliation(s)
- Daniel B Russakoff
- Department of Computer Science, Stanford University, 300 Pasteur Drive, Stanford, CA 94305-5327, USA
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Affiliation(s)
- J W Haller
- Department of Radiology, University of Iowa, Iowa City 52242, USA
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Penney GP, Batchelor PG, Hill DL, Hawkes DJ, Weese J. Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images. Med Phys 2001; 28:1024-32. [PMID: 11439472 DOI: 10.1118/1.1373400] [Citation(s) in RCA: 91] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
We present a validation of an intensity based two- to three-dimensional image registration algorithm. The algorithm can register a CT volume to a single-plane fluoroscopy image. Four routinely acquired clinical data sets from patients who underwent endovascular treatment for an abdominal aortic aneurysm were used. Each data set was comprised of two intraoperative fluoroscopy images and a preoperative CT image. Regions of interest (ROI) were drawn around each vertebra in the CT and fluoroscopy images. Each CT image ROI was individually registered to the corresponding ROI in the fluoroscopy images. A cross validation approach was used to obtain a measure of registration consistency. Spinal movement between the preoperative and intraoperative scene was accounted for by using two fluoroscopy images. The consistency and robustness of the algorithm when using two similarity measures, pattern intensity and gradient difference, was investigated. Both similarity measures produced similar results. The consistency values were rotational errors below 0.74 degree and in-plane translational errors below 0.90 mm. These errors approximately relate to a two-dimensional projection error of 1.3 mm. The failure rate was less than 8.3% for three of the four data sets. However, for one of the data sets a much larger failure rate (28.5%) occurred.
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Affiliation(s)
- G P Penney
- Computational Imaging Science Group, Division of Radiological Sciences, Kings College London, Guy's Hospital, London Bridge, London, SE1 9RT, United
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LOCALITE – A Frameless Neuronavigation System for Interventional Magnetic Resonance Imaging Systems. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI’99 1999. [DOI: 10.1007/10704282_90] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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